Overview

Dataset statistics

Number of variables34
Number of observations4566
Missing cells10
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory272.0 B

Variable types

Numeric22
Categorical12

Warnings

R_fighter has a high cardinality: 1286 distinct values High cardinality
B_fighter has a high cardinality: 1513 distinct values High cardinality
date has a high cardinality: 399 distinct values High cardinality
location has a high cardinality: 145 distinct values High cardinality
B_total_rounds_fought is highly correlated with B_winsHigh correlation
B_wins is highly correlated with B_total_rounds_foughtHigh correlation
B_Weight_lbs is highly correlated with R_Weight_lbsHigh correlation
R_Weight_lbs is highly correlated with B_Weight_lbsHigh correlation
weight_class is highly correlated with genderHigh correlation
gender is highly correlated with weight_classHigh correlation
# is uniformly distributed Uniform
date is uniformly distributed Uniform
# has unique values Unique
B_losses has 1606 (35.2%) zeros Zeros
B_total_rounds_fought has 975 (21.4%) zeros Zeros
B_wins has 1460 (32.0%) zeros Zeros
R_losses has 1069 (23.4%) zeros Zeros
R_total_rounds_fought has 428 (9.4%) zeros Zeros
R_wins has 765 (16.8%) zeros Zeros
lose_streak_dif has 2712 (59.4%) zeros Zeros
win_streak_dif has 2350 (51.5%) zeros Zeros
win_dif has 1062 (23.3%) zeros Zeros
loss_dif has 1172 (25.7%) zeros Zeros
height_dif has 787 (17.2%) zeros Zeros
reach_dif has 569 (12.5%) zeros Zeros
age_dif has 342 (7.5%) zeros Zeros

Reproduction

Analysis started2021-03-02 14:27:28.900789
Analysis finished2021-03-02 14:30:36.237770
Duration3 minutes and 7.34 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

#
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct4566
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2282.5
Minimum0
Maximum4565
Zeros1
Zeros (%)< 0.1%
Memory size35.8 KiB
2021-03-02T15:30:36.536046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile228.25
Q11141.25
median2282.5
Q33423.75
95-th percentile4336.75
Maximum4565
Range4565
Interquartile range (IQR)2282.5

Descriptive statistics

Standard deviation1318.234994
Coefficient of variation (CV)0.5775399756
Kurtosis-1.2
Mean2282.5
Median Absolute Deviation (MAD)1141.5
Skewness0
Sum10421895
Variance1737743.5
MonotocityStrictly increasing
2021-03-02T15:30:36.875772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
5291
 
< 0.1%
5171
 
< 0.1%
25681
 
< 0.1%
5211
 
< 0.1%
25721
 
< 0.1%
5251
 
< 0.1%
25761
 
< 0.1%
25801
 
< 0.1%
5131
 
< 0.1%
Other values (4556)4556
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
45651
< 0.1%
45641
< 0.1%
45631
< 0.1%
45621
< 0.1%
45611
< 0.1%

R_fighter
Categorical

HIGH CARDINALITY

Distinct1286
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Donald Cerrone
 
23
Jim Miller
 
21
Demian Maia
 
18
Dustin Poirier
 
18
Joseph Benavidez
 
17
Other values (1281)
4469 

Length

Max length25
Median length13
Mean length13.11038108
Min length7

Characters and Unicode

Total characters59862
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)9.2%

Sample

1st rowAlistair Overeem
2nd rowCory Sandhagen
3rd rowAlexandre Pantoja
4th rowDiego Ferreira
5th rowMichael Johnson
ValueCountFrequency (%)
Donald Cerrone23
 
0.5%
Jim Miller21
 
0.5%
Demian Maia18
 
0.4%
Dustin Poirier18
 
0.4%
Joseph Benavidez17
 
0.4%
Edson Barboza16
 
0.4%
Mauricio Rua16
 
0.4%
Cub Swanson16
 
0.4%
Ross Pearson16
 
0.4%
Joe Lauzon15
 
0.3%
Other values (1276)4390
96.1%
2021-03-02T15:30:37.770487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john63
 
0.7%
chris56
 
0.6%
silva54
 
0.6%
anthony51
 
0.5%
johnson50
 
0.5%
mike48
 
0.5%
michael45
 
0.5%
thiago41
 
0.4%
alex39
 
0.4%
matt39
 
0.4%
Other values (1844)8836
94.8%

Most occurring characters

ValueCountFrequency (%)
a5918
 
9.9%
e4995
 
8.3%
4757
 
7.9%
n4181
 
7.0%
i4104
 
6.9%
o3986
 
6.7%
r3778
 
6.3%
l2566
 
4.3%
s2441
 
4.1%
t1715
 
2.9%
Other values (46)21421
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45607
76.2%
Uppercase Letter9456
 
15.8%
Space Separator4757
 
7.9%
Dash Punctuation31
 
0.1%
Other Punctuation11
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
M1018
 
10.8%
J815
 
8.6%
S709
 
7.5%
C699
 
7.4%
A660
 
7.0%
D614
 
6.5%
R587
 
6.2%
B565
 
6.0%
T444
 
4.7%
P420
 
4.4%
Other values (16)2925
30.9%
ValueCountFrequency (%)
a5918
13.0%
e4995
11.0%
n4181
9.2%
i4104
 
9.0%
o3986
 
8.7%
r3778
 
8.3%
l2566
 
5.6%
s2441
 
5.4%
t1715
 
3.8%
u1499
 
3.3%
Other values (16)10424
22.9%
ValueCountFrequency (%)
'7
63.6%
.4
36.4%
ValueCountFrequency (%)
4757
100.0%
ValueCountFrequency (%)
-31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55063
92.0%
Common4799
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
a5918
 
10.7%
e4995
 
9.1%
n4181
 
7.6%
i4104
 
7.5%
o3986
 
7.2%
r3778
 
6.9%
l2566
 
4.7%
s2441
 
4.4%
t1715
 
3.1%
u1499
 
2.7%
Other values (42)19880
36.1%
ValueCountFrequency (%)
4757
99.1%
-31
 
0.6%
'7
 
0.1%
.4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII59862
100.0%

Most frequent character per block

ValueCountFrequency (%)
a5918
 
9.9%
e4995
 
8.3%
4757
 
7.9%
n4181
 
7.0%
i4104
 
6.9%
o3986
 
6.7%
r3778
 
6.3%
l2566
 
4.3%
s2441
 
4.1%
t1715
 
2.9%
Other values (46)21421
35.8%

B_fighter
Categorical

HIGH CARDINALITY

Distinct1513
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Charles Oliveira
 
18
Jeremy Stephens
 
16
Nik Lentz
 
14
Angela Hill
 
13
Donald Cerrone
 
12
Other values (1508)
4493 

Length

Max length27
Median length13
Mean length13.11410425
Min length7

Characters and Unicode

Total characters59879
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique430 ?
Unique (%)9.4%

Sample

1st rowAlexander Volkov
2nd rowFrankie Edgar
3rd rowManel Kape
4th rowBeneil Dariush
5th rowClay Guida
ValueCountFrequency (%)
Charles Oliveira18
 
0.4%
Jeremy Stephens16
 
0.4%
Nik Lentz14
 
0.3%
Angela Hill13
 
0.3%
Donald Cerrone12
 
0.3%
Rafael Dos Anjos12
 
0.3%
Kevin Lee12
 
0.3%
Sam Alvey12
 
0.3%
Jorge Masvidal11
 
0.2%
Evan Dunham11
 
0.2%
Other values (1503)4435
97.1%
2021-03-02T15:30:38.884574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mike63
 
0.7%
chris63
 
0.7%
alex58
 
0.6%
john52
 
0.6%
anthony49
 
0.5%
matt47
 
0.5%
silva41
 
0.4%
tim41
 
0.4%
de39
 
0.4%
ryan39
 
0.4%
Other values (2117)8847
94.7%

Most occurring characters

ValueCountFrequency (%)
a5963
 
10.0%
e5001
 
8.4%
4773
 
8.0%
n4215
 
7.0%
i4054
 
6.8%
o3932
 
6.6%
r3815
 
6.4%
l2656
 
4.4%
s2369
 
4.0%
t1846
 
3.1%
Other values (46)21255
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45630
76.2%
Uppercase Letter9434
 
15.8%
Space Separator4773
 
8.0%
Dash Punctuation22
 
< 0.1%
Other Punctuation20
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
M968
 
10.3%
J780
 
8.3%
S761
 
8.1%
C717
 
7.6%
A689
 
7.3%
D595
 
6.3%
R578
 
6.1%
B543
 
5.8%
P417
 
4.4%
T400
 
4.2%
Other values (16)2986
31.7%
ValueCountFrequency (%)
a5963
13.1%
e5001
11.0%
n4215
9.2%
i4054
 
8.9%
o3932
 
8.6%
r3815
 
8.4%
l2656
 
5.8%
s2369
 
5.2%
t1846
 
4.0%
h1457
 
3.2%
Other values (16)10322
22.6%
ValueCountFrequency (%)
'15
75.0%
.5
 
25.0%
ValueCountFrequency (%)
4773
100.0%
ValueCountFrequency (%)
-22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin55064
92.0%
Common4815
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
a5963
 
10.8%
e5001
 
9.1%
n4215
 
7.7%
i4054
 
7.4%
o3932
 
7.1%
r3815
 
6.9%
l2656
 
4.8%
s2369
 
4.3%
t1846
 
3.4%
h1457
 
2.6%
Other values (42)19756
35.9%
ValueCountFrequency (%)
4773
99.1%
-22
 
0.5%
'15
 
0.3%
.5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII59879
100.0%

Most frequent character per block

ValueCountFrequency (%)
a5963
 
10.0%
e5001
 
8.4%
4773
 
8.0%
n4215
 
7.0%
i4054
 
6.8%
o3932
 
6.6%
r3815
 
6.4%
l2656
 
4.4%
s2369
 
4.0%
t1846
 
3.1%
Other values (46)21255
35.5%

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct399
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
11/19/2016
 
24
10/4/2014
 
22
5/31/2014
 
22
8/23/2014
 
21
6/28/2014
 
21
Other values (394)
4456 

Length

Max length10
Median length9
Mean length8.97087166
Min length8

Characters and Unicode

Total characters40961
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2/6/2021
2nd row2/6/2021
3rd row2/6/2021
4th row2/6/2021
5th row2/6/2021
ValueCountFrequency (%)
11/19/201624
 
0.5%
10/4/201422
 
0.5%
5/31/201422
 
0.5%
8/23/201421
 
0.5%
6/28/201421
 
0.5%
7/25/202015
 
0.3%
10/12/201914
 
0.3%
1/20/202114
 
0.3%
4/14/201814
 
0.3%
4/13/201913
 
0.3%
Other values (389)4386
96.1%
2021-03-02T15:30:39.975985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/19/201624
 
0.5%
10/4/201422
 
0.5%
5/31/201422
 
0.5%
8/23/201421
 
0.5%
6/28/201421
 
0.5%
7/25/202015
 
0.3%
10/12/201914
 
0.3%
1/20/202114
 
0.3%
4/14/201814
 
0.3%
4/13/201913
 
0.3%
Other values (389)4386
96.1%

Most occurring characters

ValueCountFrequency (%)
/9132
22.3%
18497
20.7%
28006
19.5%
05990
14.6%
61369
 
3.3%
81368
 
3.3%
71361
 
3.3%
31350
 
3.3%
91319
 
3.2%
51301
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31829
77.7%
Other Punctuation9132
 
22.3%

Most frequent character per category

ValueCountFrequency (%)
18497
26.7%
28006
25.2%
05990
18.8%
61369
 
4.3%
81368
 
4.3%
71361
 
4.3%
31350
 
4.2%
91319
 
4.1%
51301
 
4.1%
41268
 
4.0%
ValueCountFrequency (%)
/9132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common40961
100.0%

Most frequent character per script

ValueCountFrequency (%)
/9132
22.3%
18497
20.7%
28006
19.5%
05990
14.6%
61369
 
3.3%
81368
 
3.3%
71361
 
3.3%
31350
 
3.3%
91319
 
3.2%
51301
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII40961
100.0%

Most frequent character per block

ValueCountFrequency (%)
/9132
22.3%
18497
20.7%
28006
19.5%
05990
14.6%
61369
 
3.3%
81368
 
3.3%
71361
 
3.3%
31350
 
3.3%
91319
 
3.2%
51301
 
3.2%

location
Categorical

HIGH CARDINALITY

Distinct145
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Las Vegas, Nevada, USA
973 
Abu Dhabi, Abu Dhabi, United Arab Emirates
 
153
Chicago, Illinois, USA
 
80
Newark, New Jersey, USA
 
79
Toronto, Ontario, Canada
 
74
Other values (140)
3207 

Length

Max length43
Median length23
Mean length25.16031537
Min length12

Characters and Unicode

Total characters114882
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLas Vegas, Nevada, USA
2nd rowLas Vegas, Nevada, USA
3rd rowLas Vegas, Nevada, USA
4th rowLas Vegas, Nevada, USA
5th rowLas Vegas, Nevada, USA
ValueCountFrequency (%)
Las Vegas, Nevada, USA973
 
21.3%
Abu Dhabi, Abu Dhabi, United Arab Emirates153
 
3.4%
Chicago, Illinois, USA80
 
1.8%
Newark, New Jersey, USA79
 
1.7%
Toronto, Ontario, Canada74
 
1.6%
London, England, United Kingdom74
 
1.6%
Stockholm, Sweden72
 
1.6%
Boston, Massachusetts, USA71
 
1.6%
Rio de Janeiro, Brazil68
 
1.5%
Sao Paulo, Sao Paulo, Brazil60
 
1.3%
Other values (135)2862
62.7%
2021-03-02T15:30:40.845358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2719
 
16.0%
nevada973
 
5.7%
vegas973
 
5.7%
las973
 
5.7%
new447
 
2.6%
brazil411
 
2.4%
canada337
 
2.0%
united335
 
2.0%
dhabi323
 
1.9%
abu323
 
1.9%
Other values (246)9203
54.1%

Most occurring characters

ValueCountFrequency (%)
a12966
 
11.3%
12451
 
10.8%
,8660
 
7.5%
e7517
 
6.5%
i6255
 
5.4%
o5387
 
4.7%
n5379
 
4.7%
s4889
 
4.3%
r4382
 
3.8%
A3877
 
3.4%
Other values (45)43119
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71425
62.2%
Uppercase Letter22300
 
19.4%
Space Separator12451
 
10.8%
Other Punctuation8671
 
7.5%
Dash Punctuation35
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a12966
18.2%
e7517
10.5%
i6255
8.8%
o5387
 
7.5%
n5379
 
7.5%
s4889
 
6.8%
r4382
 
6.1%
l3857
 
5.4%
t3385
 
4.7%
d3304
 
4.6%
Other values (16)14104
19.7%
ValueCountFrequency (%)
A3877
17.4%
S3736
16.8%
U3114
14.0%
N1743
7.8%
C1313
 
5.9%
L1225
 
5.5%
V1123
 
5.0%
B864
 
3.9%
M677
 
3.0%
D618
 
2.8%
Other values (15)4010
18.0%
ValueCountFrequency (%)
,8660
99.9%
.11
 
0.1%
ValueCountFrequency (%)
12451
100.0%
ValueCountFrequency (%)
-35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin93725
81.6%
Common21157
 
18.4%

Most frequent character per script

ValueCountFrequency (%)
a12966
 
13.8%
e7517
 
8.0%
i6255
 
6.7%
o5387
 
5.7%
n5379
 
5.7%
s4889
 
5.2%
r4382
 
4.7%
A3877
 
4.1%
l3857
 
4.1%
S3736
 
4.0%
Other values (41)35480
37.9%
ValueCountFrequency (%)
12451
58.9%
,8660
40.9%
-35
 
0.2%
.11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII114882
100.0%

Most frequent character per block

ValueCountFrequency (%)
a12966
 
11.3%
12451
 
10.8%
,8660
 
7.5%
e7517
 
6.5%
i6255
 
5.4%
o5387
 
4.7%
n5379
 
4.7%
s4889
 
4.3%
r4382
 
3.8%
A3877
 
3.4%
Other values (45)43119
37.5%

country
Categorical

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
USA
2450 
Brazil
400 
Canada
337 
USA
269 
United Kingdom
 
165
Other values (23)
945 

Length

Max length21
Median length4
Mean length6.196671047
Min length3

Characters and Unicode

Total characters28294
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
USA2450
53.7%
Brazil400
 
8.8%
Canada337
 
7.4%
USA269
 
5.9%
United Kingdom165
 
3.6%
Australia160
 
3.5%
United Arab Emirates141
 
3.1%
Sweden72
 
1.6%
Mexico70
 
1.5%
China61
 
1.3%
Other values (18)441
 
9.7%
2021-03-02T15:30:41.644312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2719
52.9%
brazil411
 
8.0%
canada337
 
6.6%
united335
 
6.5%
emirates170
 
3.3%
arab170
 
3.3%
kingdom165
 
3.2%
australia160
 
3.1%
sweden72
 
1.4%
mexico70
 
1.4%
Other values (20)532
 
10.3%

Most occurring characters

ValueCountFrequency (%)
4720
16.7%
U3067
10.8%
A3061
10.8%
S2860
10.1%
a2605
9.2%
i1540
 
5.4%
n1271
 
4.5%
r1129
 
4.0%
e1053
 
3.7%
d1009
 
3.6%
Other values (29)5979
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12995
45.9%
Uppercase Letter10579
37.4%
Space Separator4720
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
a2605
20.0%
i1540
11.9%
n1271
9.8%
r1129
8.7%
e1053
8.1%
d1009
 
7.8%
t739
 
5.7%
l709
 
5.5%
s439
 
3.4%
z424
 
3.3%
Other values (12)2077
16.0%
ValueCountFrequency (%)
U3067
29.0%
A3061
28.9%
S2860
27.0%
C437
 
4.1%
B411
 
3.9%
K189
 
1.8%
E170
 
1.6%
M70
 
0.7%
N58
 
0.5%
G54
 
0.5%
Other values (6)202
 
1.9%
ValueCountFrequency (%)
4720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23574
83.3%
Common4720
 
16.7%

Most frequent character per script

ValueCountFrequency (%)
U3067
13.0%
A3061
13.0%
S2860
12.1%
a2605
11.1%
i1540
 
6.5%
n1271
 
5.4%
r1129
 
4.8%
e1053
 
4.5%
d1009
 
4.3%
t739
 
3.1%
Other values (28)5240
22.2%
ValueCountFrequency (%)
4720
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28294
100.0%

Most frequent character per block

ValueCountFrequency (%)
4720
16.7%
U3067
10.8%
A3061
10.8%
S2860
10.1%
a2605
9.2%
i1540
 
5.4%
n1271
 
4.5%
r1129
 
4.0%
e1053
 
3.7%
d1009
 
3.6%
Other values (29)5979
21.1%

Winner
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Red
2674 
Blue
1892 

Length

Max length4
Median length3
Mean length3.414367061
Min length3

Characters and Unicode

Total characters15590
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowRed
3rd rowRed
4th rowBlue
5th rowBlue
ValueCountFrequency (%)
Red2674
58.6%
Blue1892
41.4%
2021-03-02T15:30:42.436285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:30:42.681467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
red2674
58.6%
blue1892
41.4%

Most occurring characters

ValueCountFrequency (%)
e4566
29.3%
R2674
17.2%
d2674
17.2%
B1892
12.1%
l1892
12.1%
u1892
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11024
70.7%
Uppercase Letter4566
29.3%

Most frequent character per category

ValueCountFrequency (%)
e4566
41.4%
d2674
24.3%
l1892
17.2%
u1892
17.2%
ValueCountFrequency (%)
R2674
58.6%
B1892
41.4%

Most occurring scripts

ValueCountFrequency (%)
Latin15590
100.0%

Most frequent character per script

ValueCountFrequency (%)
e4566
29.3%
R2674
17.2%
d2674
17.2%
B1892
12.1%
l1892
12.1%
u1892
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15590
100.0%

Most frequent character per block

ValueCountFrequency (%)
e4566
29.3%
R2674
17.2%
d2674
17.2%
B1892
12.1%
l1892
12.1%
u1892
12.1%

weight_class
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Lightweight
815 
Welterweight
789 
Middleweight
550 
Featherweight
530 
Bantamweight
451 
Other values (8)
1431 

Length

Max length21
Median length12
Mean length12.80332895
Min length9

Characters and Unicode

Total characters58460
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHeavyweight
2nd rowBantamweight
3rd rowFlyweight
4th rowLightweight
5th rowLightweight
ValueCountFrequency (%)
Lightweight815
17.8%
Welterweight789
17.3%
Middleweight550
12.0%
Featherweight530
11.6%
Bantamweight451
9.9%
Light Heavyweight370
8.1%
Heavyweight357
7.8%
Flyweight221
 
4.8%
Women's Strawweight184
 
4.0%
Women's Bantamweight145
 
3.2%
Other values (3)154
 
3.4%
2021-03-02T15:30:43.330096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lightweight815
15.0%
welterweight789
14.6%
heavyweight727
13.4%
bantamweight596
11.0%
middleweight550
10.1%
featherweight545
10.1%
women's452
8.3%
light370
6.8%
flyweight329
6.1%
strawweight184
 
3.4%
Other values (2)62
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e8963
15.3%
t7896
13.5%
h6327
10.8%
i6301
10.8%
g5751
9.8%
w4719
8.1%
a2679
 
4.6%
l1668
 
2.9%
r1518
 
2.6%
W1272
 
2.2%
Other values (17)11366
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51736
88.5%
Uppercase Letter5419
 
9.3%
Space Separator853
 
1.5%
Other Punctuation452
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
e8963
17.3%
t7896
15.3%
h6327
12.2%
i6301
12.2%
g5751
11.1%
w4719
9.1%
a2679
 
5.2%
l1668
 
3.2%
r1518
 
2.9%
d1100
 
2.1%
Other values (7)4814
9.3%
ValueCountFrequency (%)
W1272
23.5%
L1185
21.9%
F874
16.1%
H727
13.4%
B596
11.0%
M550
10.1%
S184
 
3.4%
C31
 
0.6%
ValueCountFrequency (%)
853
100.0%
ValueCountFrequency (%)
'452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57155
97.8%
Common1305
 
2.2%

Most frequent character per script

ValueCountFrequency (%)
e8963
15.7%
t7896
13.8%
h6327
11.1%
i6301
11.0%
g5751
10.1%
w4719
8.3%
a2679
 
4.7%
l1668
 
2.9%
r1518
 
2.7%
W1272
 
2.2%
Other values (15)10061
17.6%
ValueCountFrequency (%)
853
65.4%
'452
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII58460
100.0%

Most frequent character per block

ValueCountFrequency (%)
e8963
15.3%
t7896
13.5%
h6327
10.8%
i6301
10.8%
g5751
9.8%
w4719
8.1%
a2679
 
4.6%
l1668
 
2.9%
r1518
 
2.6%
W1272
 
2.2%
Other values (17)11366
19.4%

gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
MALE
4114 
FEMALE
452 

Length

Max length6
Median length4
Mean length4.197985107
Min length4

Characters and Unicode

Total characters19168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE
ValueCountFrequency (%)
MALE4114
90.1%
FEMALE452
 
9.9%
2021-03-02T15:30:44.060256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:30:44.319544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
male4114
90.1%
female452
 
9.9%

Most occurring characters

ValueCountFrequency (%)
E5018
26.2%
M4566
23.8%
A4566
23.8%
L4566
23.8%
F452
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter19168
100.0%

Most frequent character per category

ValueCountFrequency (%)
E5018
26.2%
M4566
23.8%
A4566
23.8%
L4566
23.8%
F452
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin19168
100.0%

Most frequent character per script

ValueCountFrequency (%)
E5018
26.2%
M4566
23.8%
A4566
23.8%
L4566
23.8%
F452
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII19168
100.0%

Most frequent character per block

ValueCountFrequency (%)
E5018
26.2%
M4566
23.8%
A4566
23.8%
L4566
23.8%
F452
 
2.4%

no_of_rounds
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
3
4144 
5
 
401
4
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4566
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%
2021-03-02T15:30:44.936272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:30:45.145730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%

Most occurring characters

ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4566
100.0%

Most frequent character per category

ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common4566
100.0%

Most frequent character per script

ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4566
100.0%

Most frequent character per block

ValueCountFrequency (%)
34144
90.8%
5401
 
8.8%
421
 
0.5%

B_losses
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.703898379
Minimum0
Maximum15
Zeros1606
Zeros (%)35.2%
Memory size35.8 KiB
2021-03-02T15:30:45.391393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.071962238
Coefficient of variation (CV)1.216012799
Kurtosis4.360154379
Mean1.703898379
Median Absolute Deviation (MAD)1
Skewness1.855807813
Sum7780
Variance4.293027516
MonotocityNot monotonic
2021-03-02T15:30:45.709291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01606
35.2%
11135
24.9%
2697
15.3%
3400
 
8.8%
4302
 
6.6%
5152
 
3.3%
696
 
2.1%
763
 
1.4%
847
 
1.0%
931
 
0.7%
Other values (6)37
 
0.8%
ValueCountFrequency (%)
01606
35.2%
11135
24.9%
2697
15.3%
3400
 
8.8%
4302
 
6.6%
ValueCountFrequency (%)
152
 
< 0.1%
141
 
< 0.1%
133
 
0.1%
123
 
0.1%
1112
0.3%

B_total_rounds_fought
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.55343846
Minimum0
Maximum97
Zeros975
Zeros (%)21.4%
Memory size35.8 KiB
2021-03-02T15:30:46.104297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q315
95-th percentile37
Maximum97
Range97
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.94387972
Coefficient of variation (CV)1.226508286
Kurtosis5.800572614
Mean10.55343846
Median Absolute Deviation (MAD)6
Skewness2.125607899
Sum48187
Variance167.5440221
MonotocityNot monotonic
2021-03-02T15:30:46.485000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0975
21.4%
3446
 
9.8%
6265
 
5.8%
1203
 
4.4%
9176
 
3.9%
4172
 
3.8%
7171
 
3.7%
2151
 
3.3%
5150
 
3.3%
12134
 
2.9%
Other values (71)1723
37.7%
ValueCountFrequency (%)
0975
21.4%
1203
 
4.4%
2151
 
3.3%
3446
9.8%
4172
 
3.8%
ValueCountFrequency (%)
971
< 0.1%
941
< 0.1%
911
< 0.1%
891
< 0.1%
871
< 0.1%

B_wins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.833114323
Minimum0
Maximum31
Zeros1460
Zeros (%)32.0%
Memory size35.8 KiB
2021-03-02T15:30:46.829996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum31
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.595568439
Coefficient of variation (CV)1.269122255
Kurtosis5.124419643
Mean2.833114323
Median Absolute Deviation (MAD)2
Skewness1.989025232
Sum12936
Variance12.9281124
MonotocityNot monotonic
2021-03-02T15:30:47.106389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
01460
32.0%
1810
17.7%
2525
 
11.5%
3429
 
9.4%
4321
 
7.0%
5210
 
4.6%
6199
 
4.4%
7133
 
2.9%
9109
 
2.4%
8105
 
2.3%
Other values (16)265
 
5.8%
ValueCountFrequency (%)
01460
32.0%
1810
17.7%
2525
 
11.5%
3429
 
9.4%
4321
 
7.0%
ValueCountFrequency (%)
311
< 0.1%
291
< 0.1%
232
< 0.1%
222
< 0.1%
212
< 0.1%

B_Stance
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Orthodox
3439 
Southpaw
919 
Switch
 
206
Switch
 
1
Open Stance
 
1

Length

Max length11
Median length8
Mean length7.910205869
Min length6

Characters and Unicode

Total characters36118
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOrthodox
2nd rowOrthodox
3rd rowSouthpaw
4th rowSouthpaw
5th rowOrthodox
ValueCountFrequency (%)
Orthodox3439
75.3%
Southpaw919
 
20.1%
Switch206
 
4.5%
Switch 1
 
< 0.1%
Open Stance1
 
< 0.1%
2021-03-02T15:30:47.875582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:30:48.084633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3439
75.3%
southpaw919
 
20.1%
switch207
 
4.5%
open1
 
< 0.1%
stance1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o7797
21.6%
t4566
12.6%
h4565
12.6%
O3440
9.5%
r3439
9.5%
d3439
9.5%
x3439
9.5%
S1127
 
3.1%
w1126
 
3.1%
p920
 
2.5%
Other values (7)2260
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31549
87.3%
Uppercase Letter4567
 
12.6%
Space Separator2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o7797
24.7%
t4566
14.5%
h4565
14.5%
r3439
10.9%
d3439
10.9%
x3439
10.9%
w1126
 
3.6%
p920
 
2.9%
a920
 
2.9%
u919
 
2.9%
Other values (4)419
 
1.3%
ValueCountFrequency (%)
O3440
75.3%
S1127
 
24.7%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36116
> 99.9%
Common2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o7797
21.6%
t4566
12.6%
h4565
12.6%
O3440
9.5%
r3439
9.5%
d3439
9.5%
x3439
9.5%
S1127
 
3.1%
w1126
 
3.1%
p920
 
2.5%
Other values (6)2258
 
6.3%
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36118
100.0%

Most frequent character per block

ValueCountFrequency (%)
o7797
21.6%
t4566
12.6%
h4565
12.6%
O3440
9.5%
r3439
9.5%
d3439
9.5%
x3439
9.5%
S1127
 
3.1%
w1126
 
3.1%
p920
 
2.5%
Other values (7)2260
 
6.3%

B_Height_cms
Real number (ℝ≥0)

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.1669908
Minimum152.4
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:48.412832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile162.56
Q1172.72
median177.8
Q3185.42
95-th percentile193.04
Maximum210.82
Range58.42
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation8.877165174
Coefficient of variation (CV)0.04982497113
Kurtosis-0.2767186382
Mean178.1669908
Median Absolute Deviation (MAD)5.08
Skewness-0.09120993051
Sum813510.48
Variance78.80406152
MonotocityNot monotonic
2021-03-02T15:30:48.688539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
177.8526
11.5%
182.88509
11.1%
180.34447
9.8%
172.72411
9.0%
175.26392
8.6%
185.42384
8.4%
170.18325
7.1%
167.64320
7.0%
187.96302
6.6%
190.5278
6.1%
Other values (13)672
14.7%
ValueCountFrequency (%)
152.45
 
0.1%
154.9431
 
0.7%
157.4810
 
0.2%
160.0294
2.1%
162.56113
2.5%
ValueCountFrequency (%)
210.826
 
0.1%
203.22
 
< 0.1%
200.6613
 
0.3%
198.1222
0.5%
195.5844
1.0%

B_Reach_cms
Real number (ℝ≥0)

Distinct58
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.4772952
Minimum0
Maximum213.36
Zeros1
Zeros (%)< 0.1%
Memory size35.8 KiB
2021-03-02T15:30:49.075451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165.1
Q1175.26
median182.88
Q3190.5
95-th percentile200.66
Maximum213.36
Range213.36
Interquartile range (IQR)15.24

Descriptive statistics

Standard deviation10.93330324
Coefficient of variation (CV)0.05991596504
Kurtosis16.48418515
Mean182.4772952
Median Absolute Deviation (MAD)7.62
Skewness-1.110185603
Sum833191.33
Variance119.5371198
MonotocityNot monotonic
2021-03-02T15:30:50.420208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.8434
 
9.5%
182.88419
 
9.2%
185.42414
 
9.1%
180.34408
 
8.9%
187.96407
 
8.9%
190.5353
 
7.7%
193.04296
 
6.5%
172.72251
 
5.5%
175.26228
 
5.0%
170.18191
 
4.2%
Other values (48)1165
25.5%
ValueCountFrequency (%)
01
 
< 0.1%
147.321
 
< 0.1%
152.48
0.2%
154.944
0.1%
1572
 
< 0.1%
ValueCountFrequency (%)
213.3610
0.2%
210.825
 
0.1%
208.2820
0.4%
2062
 
< 0.1%
205.7424
0.5%

B_Weight_lbs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.9943057
Minimum115
Maximum265
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:50.795327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile125
Q1135
median155
Q3185
95-th percentile245.75
Maximum265
Range150
Interquartile range (IQR)50

Descriptive statistics

Standard deviation34.17748871
Coefficient of variation (CV)0.2071434439
Kurtosis1.01536438
Mean164.9943057
Median Absolute Deviation (MAD)20
Skewness1.082611859
Sum753364
Variance1168.100734
MonotocityNot monotonic
2021-03-02T15:30:51.122046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
170826
18.1%
155823
18.0%
135612
13.4%
145515
11.3%
185514
11.3%
205402
8.8%
125350
7.7%
115182
 
4.0%
26572
 
1.6%
25042
 
0.9%
Other values (25)228
 
5.0%
ValueCountFrequency (%)
115182
 
4.0%
125350
7.7%
135612
13.4%
1402
 
< 0.1%
145515
11.3%
ValueCountFrequency (%)
26572
1.6%
26422
 
0.5%
2637
 
0.2%
2622
 
< 0.1%
26031
0.7%

R_losses
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.344064827
Minimum0
Maximum17
Zeros1069
Zeros (%)23.4%
Memory size35.8 KiB
2021-03-02T15:30:51.478521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.430027299
Coefficient of variation (CV)1.036672395
Kurtosis3.617792068
Mean2.344064827
Median Absolute Deviation (MAD)1
Skewness1.646652917
Sum10703
Variance5.905032674
MonotocityNot monotonic
2021-03-02T15:30:51.751117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
01069
23.4%
11059
23.2%
2788
17.3%
3569
12.5%
4356
 
7.8%
5253
 
5.5%
6176
 
3.9%
790
 
2.0%
876
 
1.7%
949
 
1.1%
Other values (8)81
 
1.8%
ValueCountFrequency (%)
01069
23.4%
11059
23.2%
2788
17.3%
3569
12.5%
4356
 
7.8%
ValueCountFrequency (%)
172
 
< 0.1%
163
 
0.1%
153
 
0.1%
143
 
0.1%
138
0.2%

R_total_rounds_fought
Real number (ℝ≥0)

ZEROS

Distinct88
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65571616
Minimum0
Maximum448
Zeros428
Zeros (%)9.4%
Memory size35.8 KiB
2021-03-02T15:30:52.117208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q323
95-th percentile47
Maximum448
Range448
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.62349507
Coefficient of variation (CV)1.061816329
Kurtosis101.4115306
Mean15.65571616
Median Absolute Deviation (MAD)8
Skewness5.04256817
Sum71484
Variance276.3405883
MonotocityNot monotonic
2021-03-02T15:30:52.660386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0428
 
9.4%
3343
 
7.5%
6244
 
5.3%
4179
 
3.9%
9173
 
3.8%
1164
 
3.6%
5160
 
3.5%
7153
 
3.4%
2135
 
3.0%
10134
 
2.9%
Other values (78)2453
53.7%
ValueCountFrequency (%)
0428
9.4%
1164
 
3.6%
2135
 
3.0%
3343
7.5%
4179
3.9%
ValueCountFrequency (%)
4481
< 0.1%
951
< 0.1%
882
< 0.1%
861
< 0.1%
852
< 0.1%

R_wins
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.210687692
Minimum0
Maximum33
Zeros765
Zeros (%)16.8%
Memory size35.8 KiB
2021-03-02T15:30:53.012051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile13
Maximum33
Range33
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.216519914
Coefficient of variation (CV)1.0013851
Kurtosis2.934189864
Mean4.210687692
Median Absolute Deviation (MAD)2
Skewness1.490848613
Sum19226
Variance17.77904018
MonotocityNot monotonic
2021-03-02T15:30:53.314556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0765
16.8%
1734
16.1%
2557
12.2%
3482
10.6%
4379
8.3%
5314
6.9%
6246
 
5.4%
7212
 
4.6%
8171
 
3.7%
9168
 
3.7%
Other values (18)538
11.8%
ValueCountFrequency (%)
0765
16.8%
1734
16.1%
2557
12.2%
3482
10.6%
4379
8.3%
ValueCountFrequency (%)
331
< 0.1%
322
< 0.1%
291
< 0.1%
271
< 0.1%
261
< 0.1%

R_Stance
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Orthodox
3482 
Southpaw
923 
Switch
 
157
Open Stance
 
4

Length

Max length11
Median length8
Mean length7.933858958
Min length6

Characters and Unicode

Total characters36226
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthodox
2nd rowSwitch
3rd rowOrthodox
4th rowOrthodox
5th rowSouthpaw
ValueCountFrequency (%)
Orthodox3482
76.3%
Southpaw923
 
20.2%
Switch157
 
3.4%
Open Stance4
 
0.1%
2021-03-02T15:30:54.109836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:30:54.318173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3482
76.2%
southpaw923
 
20.2%
switch157
 
3.4%
open4
 
0.1%
stance4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o7887
21.8%
t4566
12.6%
h4562
12.6%
O3486
9.6%
r3482
9.6%
d3482
9.6%
x3482
9.6%
S1084
 
3.0%
w1080
 
3.0%
p927
 
2.6%
Other values (7)2188
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31652
87.4%
Uppercase Letter4570
 
12.6%
Space Separator4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o7887
24.9%
t4566
14.4%
h4562
14.4%
r3482
11.0%
d3482
11.0%
x3482
11.0%
w1080
 
3.4%
p927
 
2.9%
a927
 
2.9%
u923
 
2.9%
Other values (4)334
 
1.1%
ValueCountFrequency (%)
O3486
76.3%
S1084
 
23.7%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin36222
> 99.9%
Common4
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o7887
21.8%
t4566
12.6%
h4562
12.6%
O3486
9.6%
r3482
9.6%
d3482
9.6%
x3482
9.6%
S1084
 
3.0%
w1080
 
3.0%
p927
 
2.6%
Other values (6)2184
 
6.0%
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36226
100.0%

Most frequent character per block

ValueCountFrequency (%)
o7887
21.8%
t4566
12.6%
h4562
12.6%
O3486
9.6%
r3482
9.6%
d3482
9.6%
x3482
9.6%
S1084
 
3.0%
w1080
 
3.0%
p927
 
2.6%
Other values (7)2188
 
6.0%

R_Height_cms
Real number (ℝ≥0)

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.0826851
Minimum152.4
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:54.701269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile162.56
Q1172.72
median177.8
Q3185.42
95-th percentile193.04
Maximum210.82
Range58.42
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.003461643
Coefficient of variation (CV)0.05055775995
Kurtosis-0.1689681118
Mean178.0826851
Median Absolute Deviation (MAD)7.62
Skewness-0.01369920577
Sum813125.54
Variance81.06232155
MonotocityNot monotonic
2021-03-02T15:30:55.029549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
182.88500
11.0%
180.34452
9.9%
177.8439
9.6%
185.42438
9.6%
175.26413
9.0%
172.72400
8.8%
167.64372
8.1%
170.18325
7.1%
187.96292
6.4%
190.5258
 
5.7%
Other values (12)677
14.8%
ValueCountFrequency (%)
152.42
 
< 0.1%
154.9438
 
0.8%
157.489
 
0.2%
160.0270
1.5%
162.56144
3.2%
ValueCountFrequency (%)
210.8214
 
0.3%
200.6611
 
0.2%
198.1226
0.6%
1961
 
< 0.1%
195.5838
0.8%

R_Reach_cms
Real number (ℝ≥0)

Distinct50
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.6416404
Minimum152.4
Maximum214.63
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:55.484657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile165.1
Q1175.26
median182.88
Q3190.5
95-th percentile200.66
Maximum214.63
Range62.23
Interquartile range (IQR)15.24

Descriptive statistics

Standard deviation10.84408987
Coefficient of variation (CV)0.05937358998
Kurtosis-0.1355873869
Mean182.6416404
Median Absolute Deviation (MAD)7.62
Skewness0.0007562186238
Sum833941.73
Variance117.5942851
MonotocityNot monotonic
2021-03-02T15:30:55.943771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185.42442
 
9.7%
177.8427
 
9.4%
180.34409
 
9.0%
182.88393
 
8.6%
187.96380
 
8.3%
190.5375
 
8.2%
193.04286
 
6.3%
172.72272
 
6.0%
175.26238
 
5.2%
195.58190
 
4.2%
Other values (40)1154
25.3%
ValueCountFrequency (%)
152.412
 
0.3%
154.944
 
0.1%
1571
 
< 0.1%
157.4825
 
0.5%
160.0276
1.7%
ValueCountFrequency (%)
214.631
 
< 0.1%
213.3629
0.6%
2111
 
< 0.1%
210.828
 
0.2%
208.2815
0.3%

R_Weight_lbs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.4623303
Minimum115
Maximum265
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:56.296641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile125
Q1145
median155
Q3185
95-th percentile245
Maximum265
Range150
Interquartile range (IQR)40

Descriptive statistics

Standard deviation34.43285773
Coefficient of variation (CV)0.2081008872
Kurtosis0.9772718798
Mean165.4623303
Median Absolute Deviation (MAD)20
Skewness1.080644731
Sum755501
Variance1185.621691
MonotocityNot monotonic
2021-03-02T15:30:56.610864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
170843
18.5%
155789
17.3%
135596
13.1%
185541
11.8%
145529
11.6%
205376
8.2%
125360
7.9%
115169
 
3.7%
26581
 
1.8%
24055
 
1.2%
Other values (23)227
 
5.0%
ValueCountFrequency (%)
115169
 
3.7%
125360
7.9%
135596
13.1%
1401
 
< 0.1%
145529
11.6%
ValueCountFrequency (%)
26581
1.8%
26420
 
0.4%
26312
 
0.3%
2623
 
0.1%
26028
 
0.6%

R_age
Real number (ℝ≥0)

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.97787998
Minimum19
Maximum47
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:56.962275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile24
Q127
median30
Q333
95-th percentile37
Maximum47
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.093720404
Coefficient of variation (CV)0.1365580357
Kurtosis-0.1049712127
Mean29.97787998
Median Absolute Deviation (MAD)3
Skewness0.2479071247
Sum136879
Variance16.75854674
MonotocityNot monotonic
2021-03-02T15:30:57.240959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
30438
9.6%
29438
9.6%
31414
9.1%
28390
 
8.5%
32388
 
8.5%
27369
 
8.1%
26322
 
7.1%
33281
 
6.2%
34269
 
5.9%
25231
 
5.1%
Other values (18)1026
22.5%
ValueCountFrequency (%)
194
 
0.1%
2017
 
0.4%
2122
 
0.5%
2260
1.3%
23114
2.5%
ValueCountFrequency (%)
471
 
< 0.1%
451
 
< 0.1%
443
 
0.1%
432
 
< 0.1%
4210
0.2%

B_age
Real number (ℝ≥0)

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.46890057
Minimum19
Maximum47
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-03-02T15:30:57.632367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23
Q127
median29
Q332
95-th percentile36
Maximum47
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.991093147
Coefficient of variation (CV)0.1354340702
Kurtosis0.2426042095
Mean29.46890057
Median Absolute Deviation (MAD)3
Skewness0.3935522775
Sum134555
Variance15.92882451
MonotocityNot monotonic
2021-03-02T15:30:57.904434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
28479
10.5%
29461
10.1%
30444
9.7%
31412
9.0%
27409
9.0%
26343
 
7.5%
32322
 
7.1%
25277
 
6.1%
33253
 
5.5%
34212
 
4.6%
Other values (19)954
20.9%
ValueCountFrequency (%)
192
 
< 0.1%
2016
 
0.4%
2131
 
0.7%
2271
1.6%
23146
3.2%
ValueCountFrequency (%)
471
 
< 0.1%
461
 
< 0.1%
452
 
< 0.1%
443
 
0.1%
438
0.2%

lose_streak_dif
Real number (ℝ)

ZEROS

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1300919842
Minimum-5
Maximum6
Zeros2712
Zeros (%)59.4%
Memory size35.8 KiB
2021-03-02T15:30:58.214474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-1
Q10
median0
Q30
95-th percentile2
Maximum6
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9820653919
Coefficient of variation (CV)7.54900771
Kurtosis3.666590269
Mean0.1300919842
Median Absolute Deviation (MAD)0
Skewness0.1568000206
Sum594
Variance0.964452434
MonotocityNot monotonic
2021-03-02T15:30:58.476102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02712
59.4%
1800
 
17.5%
-1533
 
11.7%
2241
 
5.3%
-2133
 
2.9%
371
 
1.6%
-340
 
0.9%
417
 
0.4%
-413
 
0.3%
-53
 
0.1%
Other values (2)3
 
0.1%
ValueCountFrequency (%)
-53
 
0.1%
-413
 
0.3%
-340
 
0.9%
-2133
 
2.9%
-1533
11.7%
ValueCountFrequency (%)
62
 
< 0.1%
51
 
< 0.1%
417
 
0.4%
371
 
1.6%
2241
5.3%

win_streak_dif
Real number (ℝ)

ZEROS

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1800262812
Minimum-12
Maximum9
Zeros2350
Zeros (%)51.5%
Memory size35.8 KiB
2021-03-02T15:30:58.804897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-3
Q1-1
median0
Q30
95-th percentile2
Maximum9
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.710224289
Coefficient of variation (CV)-9.499859007
Kurtosis8.045801834
Mean-0.1800262812
Median Absolute Deviation (MAD)0
Skewness-1.08163318
Sum-822
Variance2.924867119
MonotocityNot monotonic
2021-03-02T15:30:59.082109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
02350
51.5%
-1693
 
15.2%
1501
 
11.0%
-2306
 
6.7%
2226
 
4.9%
-3130
 
2.8%
3112
 
2.5%
457
 
1.2%
-456
 
1.2%
-534
 
0.7%
Other values (12)101
 
2.2%
ValueCountFrequency (%)
-122
 
< 0.1%
-116
0.1%
-106
0.1%
-94
 
0.1%
-813
0.3%
ValueCountFrequency (%)
91
 
< 0.1%
82
 
< 0.1%
74
 
0.1%
612
0.3%
521
0.5%

win_dif
Real number (ℝ)

ZEROS

Distinct41
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.377573368
Minimum-28
Maximum23
Zeros1062
Zeros (%)23.3%
Memory size35.8 KiB
2021-03-02T15:30:59.388555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-28
5-th percentile-9
Q1-3
median-1
Q30
95-th percentile4.75
Maximum23
Range51
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.97740231
Coefficient of variation (CV)-2.887252615
Kurtosis4.091883262
Mean-1.377573368
Median Absolute Deviation (MAD)2
Skewness-0.4586614003
Sum-6290
Variance15.81972914
MonotocityNot monotonic
2021-03-02T15:30:59.744180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
01062
23.3%
-1686
15.0%
-2464
10.2%
-3362
 
7.9%
1354
 
7.8%
-4257
 
5.6%
2209
 
4.6%
-5180
 
3.9%
-6149
 
3.3%
3123
 
2.7%
Other values (31)720
15.8%
ValueCountFrequency (%)
-281
< 0.1%
-271
< 0.1%
-262
< 0.1%
-221
< 0.1%
-202
< 0.1%
ValueCountFrequency (%)
231
 
< 0.1%
191
 
< 0.1%
154
 
0.1%
143
 
0.1%
1310
0.2%

loss_dif
Real number (ℝ)

ZEROS

Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4724047306
Minimum-17
Maximum16
Zeros1172
Zeros (%)25.7%
Memory size35.8 KiB
2021-03-02T15:31:00.092260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile-4
Q1-1
median0
Q32
95-th percentile5
Maximum16
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.784483017
Coefficient of variation (CV)5.894274203
Kurtosis3.269969668
Mean0.4724047306
Median Absolute Deviation (MAD)1
Skewness-0.1074935121
Sum2157
Variance7.753345673
MonotocityNot monotonic
2021-03-02T15:31:00.475399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
01172
25.7%
1773
16.9%
-1516
11.3%
2490
10.7%
-2350
 
7.7%
3333
 
7.3%
4188
 
4.1%
-3162
 
3.5%
5116
 
2.5%
-498
 
2.1%
Other values (23)368
 
8.1%
ValueCountFrequency (%)
-171
< 0.1%
-161
< 0.1%
-151
< 0.1%
-141
< 0.1%
-131
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
142
< 0.1%
131
 
< 0.1%
124
0.1%
113
0.1%

height_dif
Real number (ℝ)

ZEROS

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04663600526
Minimum-187.96
Maximum30.48
Zeros787
Zeros (%)17.2%
Memory size35.8 KiB
2021-03-02T15:31:00.834199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-187.96
5-th percentile-10.16
Q1-5.08
median0
Q35.08
95-th percentile10.16
Maximum30.48
Range218.44
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation7.014511925
Coefficient of variation (CV)150.4097936
Kurtosis112.6925035
Mean0.04663600526
Median Absolute Deviation (MAD)5.08
Skewness-4.26219107
Sum212.94
Variance49.20337755
MonotocityNot monotonic
2021-03-02T15:31:01.137327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0787
17.2%
2.54696
15.2%
-2.54657
14.4%
5.08519
11.4%
-5.08504
11.0%
7.62355
7.8%
-7.62317
6.9%
10.16194
 
4.2%
-10.16185
 
4.1%
-12.7107
 
2.3%
Other values (16)245
 
5.4%
ValueCountFrequency (%)
-187.961
 
< 0.1%
-33.021
 
< 0.1%
-27.941
 
< 0.1%
-25.41
 
< 0.1%
-22.863
0.1%
ValueCountFrequency (%)
30.481
 
< 0.1%
27.941
 
< 0.1%
22.862
 
< 0.1%
20.324
 
0.1%
17.7817
0.4%

reach_dif
Real number (ℝ)

ZEROS

Distinct122
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2365045992
Minimum-187.96
Maximum30.48
Zeros569
Zeros (%)12.5%
Memory size35.8 KiB
2021-03-02T15:31:01.462806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-187.96
5-th percentile-15.24
Q1-5.08
median0
Q35.08
95-th percentile12.7
Maximum30.48
Range218.44
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation9.52406539
Coefficient of variation (CV)-40.27010647
Kurtosis82.45301027
Mean-0.2365045992
Median Absolute Deviation (MAD)5.08
Skewness-4.449100236
Sum-1079.88
Variance90.70782156
MonotocityNot monotonic
2021-03-02T15:31:01.792060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0569
12.5%
-2.54554
12.1%
2.54552
12.1%
5.08445
9.7%
-5.08401
8.8%
7.62356
7.8%
-7.62345
7.6%
10.16245
 
5.4%
-10.16209
 
4.6%
12.7163
 
3.6%
Other values (112)727
15.9%
ValueCountFrequency (%)
-187.962
< 0.1%
-160.021
< 0.1%
-33.021
< 0.1%
-30.482
< 0.1%
-27.942
< 0.1%
ValueCountFrequency (%)
30.481
 
< 0.1%
27.941
 
< 0.1%
25.46
0.1%
22.867
0.2%
20.581
 
< 0.1%

age_dif
Real number (ℝ)

ZEROS

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4213753833
Minimum-16
Maximum17
Zeros342
Zeros (%)7.5%
Memory size35.8 KiB
2021-03-02T15:31:02.115989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile-8
Q1-3
median0
Q34
95-th percentile9
Maximum17
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.144000296
Coefficient of variation (CV)12.20764312
Kurtosis-0.05783776904
Mean0.4213753833
Median Absolute Deviation (MAD)3
Skewness0.01609504017
Sum1924
Variance26.46073905
MonotocityNot monotonic
2021-03-02T15:31:02.469151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
-1365
 
8.0%
0342
 
7.5%
1333
 
7.3%
-2328
 
7.2%
2328
 
7.2%
-3315
 
6.9%
3303
 
6.6%
4259
 
5.7%
-4249
 
5.5%
5234
 
5.1%
Other values (24)1510
33.1%
ValueCountFrequency (%)
-161
 
< 0.1%
-153
 
0.1%
-149
 
0.2%
-1314
0.3%
-1229
0.6%
ValueCountFrequency (%)
172
 
< 0.1%
163
 
0.1%
156
 
0.1%
1419
0.4%
1318
0.4%

better_rank
Categorical

Distinct3
Distinct (%)0.1%
Missing10
Missing (%)0.2%
Memory size35.8 KiB
neither
3289 
Red
1196 
Blue
 
71

Length

Max length7
Median length7
Mean length5.903204565
Min length3

Characters and Unicode

Total characters26895
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed
2nd rowRed
3rd rowRed
4th rowRed
5th rowneither
ValueCountFrequency (%)
neither3289
72.0%
Red1196
 
26.2%
Blue71
 
1.6%
(Missing)10
 
0.2%
2021-03-02T15:31:03.246808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-02T15:31:03.504394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
neither3289
72.2%
red1196
 
26.3%
blue71
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e7845
29.2%
n3289
12.2%
i3289
12.2%
t3289
12.2%
h3289
12.2%
r3289
12.2%
R1196
 
4.4%
d1196
 
4.4%
B71
 
0.3%
l71
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25628
95.3%
Uppercase Letter1267
 
4.7%

Most frequent character per category

ValueCountFrequency (%)
e7845
30.6%
n3289
12.8%
i3289
12.8%
t3289
12.8%
h3289
12.8%
r3289
12.8%
d1196
 
4.7%
l71
 
0.3%
u71
 
0.3%
ValueCountFrequency (%)
R1196
94.4%
B71
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin26895
100.0%

Most frequent character per script

ValueCountFrequency (%)
e7845
29.2%
n3289
12.2%
i3289
12.2%
t3289
12.2%
h3289
12.2%
r3289
12.2%
R1196
 
4.4%
d1196
 
4.4%
B71
 
0.3%
l71
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26895
100.0%

Most frequent character per block

ValueCountFrequency (%)
e7845
29.2%
n3289
12.2%
i3289
12.2%
t3289
12.2%
h3289
12.2%
r3289
12.2%
R1196
 
4.4%
d1196
 
4.4%
B71
 
0.3%
l71
 
0.3%

Interactions

2021-03-02T15:27:36.702970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:37.069194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:37.444319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:37.866723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:38.229953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:38.559036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:38.950172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:39.308210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:39.627404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:39.972088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:40.316852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:40.675189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:41.022467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:41.325321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:41.645964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:41.990264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:42.307834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:42.823683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:43.346918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:43.684000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:44.017605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:44.342685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:44.698773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:45.105610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:45.544716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:45.973093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:46.416601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:46.909706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:47.721781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:48.195657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:48.673408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:49.236576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:49.650675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:50.095903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:50.562232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:51.035042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:51.518129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:51.968030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:52.544749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:53.026745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:53.472909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:53.851460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:54.331013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:54.730185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:55.171675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:55.594773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:56.028546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:56.462126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:56.937902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:57.511044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:57.914376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:58.399925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:58.936063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:27:59.447717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:00.128886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:00.764452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:01.191026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:01.566274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:01.944340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:02.379894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:02.943935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:03.380596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:03.823150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:04.581943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:04.957515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:05.379420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:05.837688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:06.307004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:06.730454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:07.177208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:07.704144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:08.054631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:08.401001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:08.762829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:09.151846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:09.503379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:09.843405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:10.182858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:10.521559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:10.870760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:11.203515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:11.548348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:11.859859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:12.173516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:12.620375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:12.948983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:13.318902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:13.685431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:14.072156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:14.464539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:14.977183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:15.455289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:15.918408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:16.293505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:16.637588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:16.992675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:17.419451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:17.853130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:18.228772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:18.587581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:18.922446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:19.307239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:19.665705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:28:19.989840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-02T15:29:34.661254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:35.126379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:35.526812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:35.898571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:36.209510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:36.505583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:36.834725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:37.158803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:37.493334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:37.839039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:38.169331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:38.496244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:38.861854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:39.177737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:39.511446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:39.863062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:40.173033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:40.522836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:40.891401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:41.203812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:41.547973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:41.880850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:42.226074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:42.706016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:43.015982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:43.333709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:43.665669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:43.982027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:44.314730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:44.613878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:44.914140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:45.250887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:45.548265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:45.875146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:46.182561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:46.500354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:46.819422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:47.111354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:47.455829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:47.879274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:48.177738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:48.491699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:48.853972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:49.187500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:49.508579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:49.824028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:50.142790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:50.482639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:50.856731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:51.232830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:51.557909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:51.878297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:52.186549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:52.497540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:52.854028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:53.254149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:53.601560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:53.931570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:54.248648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:54.592300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:54.937245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:55.251203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:55.579043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:55.931921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:56.291559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:56.630393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:56.956213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:57.283671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:57.679373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:58.001679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:58.347132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:58.675865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:58.979936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:59.334028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:29:59.676947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:00.056484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:00.469377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:00.859213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:01.247011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:01.580471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:01.937464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:02.295632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:02.620713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:03.110383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:03.492787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:03.851836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:04.221837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:04.618934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:04.950672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:05.292918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:05.609075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:05.929155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:06.239890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:06.628698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:07.025850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:07.396942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:07.837052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:08.972574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:09.314006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:09.657332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:09.936858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:10.274938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:10.604781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:10.893009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:11.204595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:11.508029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:11.809697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:12.127261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:12.457727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:12.766947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:13.087611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:13.402839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:13.719137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:14.038992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:14.322639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:14.633642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:14.915725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:15.219528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:15.535327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:15.836817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:16.153909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:16.450946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:16.776430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:17.095982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:17.459668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:17.778044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:18.094136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:18.410216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:18.780130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:19.084959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:19.380300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:19.687172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:19.997284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:20.311637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:20.640791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:20.929135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:21.232994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:21.581085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:21.946790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:22.295993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:22.866158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:23.319365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:23.757376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:24.219194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:24.604722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:24.932961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:25.276026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:25.646039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:26.004497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:26.381520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:26.734980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:27.058563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:27.467468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:27.859724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:28.200808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:28.549464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-02T15:30:28.902502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-02T15:31:03.835767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-02T15:31:04.828407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-02T15:31:05.860341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-02T15:31:06.877112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-02T15:31:07.981866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-02T15:30:29.699506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-02T15:30:34.748360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-02T15:30:35.563394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

#R_fighterB_fighterdatelocationcountryWinnerweight_classgenderno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Reach_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Reach_cmsR_Weight_lbsR_ageB_agelose_streak_difwin_streak_difwin_difloss_difheight_difreach_difage_difbetter_rank
00Alistair OvereemAlexander Volkov2/6/2021Las Vegas, Nevada, USAUSABlueHeavyweightMALE52266Orthodox200.66203.20250158333Orthodox193.04203.2026540320-1-27-137.620.00-8Red
11Cory SandhagenFrankie Edgar2/6/2021Las Vegas, Nevada, USAUSARedBantamweightMALE389418Orthodox167.64172.721351146Switch180.34177.80135283900127-12.70-5.0811Red
22Alexandre PantojaManel Kape2/6/2021Las Vegas, Nevada, USAUSARedFlyweightMALE3000Southpaw165.10172.721253216Orthodox165.10170.181253027-10-6-30.002.54-3Red
33Diego FerreiraBeneil Dariush2/6/2021Las Vegas, Nevada, USAUSABlueLightweightMALE343613Southpaw177.80182.881552218Orthodox175.26187.9615536310-1522.54-5.08-5Red
44Michael JohnsonClay Guida2/6/2021Las Vegas, Nevada, USAUSABlueLightweightMALE3158717Orthodox170.18177.80155125811Southpaw177.80185.421553439-1063-7.62-7.625neither
55Mike RodriguezDanilo Marques2/6/2021Las Vegas, Nevada, USAUSABlueLight HeavyweightMALE3031Orthodox198.12195.582053133Southpaw193.04208.282053235-11-2-35.08-12.703neither
66Molly McCannLara Procopio2/6/2021Las Vegas, Nevada, USAUSABlueWomen's FlyweightFEMALE3130Orthodox162.56170.181252143Orthodox162.56157.48125302500-3-10.0012.70-5neither
77SeungWoo ChoiYoussef Zalal2/6/2021Las Vegas, Nevada, USAUSARedFeatherweightMALE31123Switch177.80182.88145291Orthodox182.88187.9614528241-12-1-5.08-5.08-4neither
88Dustin PoirierConor McGregor1/23/2021Abu Dhabi, Abu Dhabi, United Arab EmiratesUnited Arab EmiratesRedLightweightMALE522510Southpaw175.26187.9615566219Southpaw175.26182.88155323200-9-40.005.080NaN
99Dan HookerMichael Chandler1/23/2021Abu Dhabi, Abu Dhabi, United Arab EmiratesUnited Arab EmiratesBlueLightweightMALE3032Orthodox172.72180.3415553710Switch182.88190.501553034-12-8-5-10.16-10.164NaN

Last rows

#R_fighterB_fighterdatelocationcountryWinnerweight_classgenderno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Reach_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Reach_cmsR_Weight_lbsR_ageB_agelose_streak_difwin_streak_difwin_difloss_difheight_difreach_difage_difbetter_rank
45564556Junior Dos SantosGabriel Gonzaga3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE33167Orthodox187.96193.04242064Orthodox193.04195.5823826300-33-3-5.08-2.54-4neither
45574557Cheick KongoPaul Buentello3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE3283Orthodox187.96195.582454227Orthodox193.04208.28240343610-42-5.08-12.70-2neither
45584558Alessio SakaraJames Irvin3/21/2010Broomfield, Colorado, USAUSARedMiddleweightMALE34104Orthodox187.96190.502055155Orthodox182.88182.881852831-1-2-115.087.62-3neither
45594559Clay GuidaShannon Gugerty3/21/2010Broomfield, Colorado, USAUSARedLightweightMALE3272Orthodox177.80180.341555265Orthodox170.18177.80155282810-337.622.540neither
45604560Eliot MarshallVladimir Matyushenko3/21/2010Broomfield, Colorado, USAUSABlueLight HeavyweightMALE32164Orthodox182.88187.96205073Orthodox187.96195.5820529390-21-2-5.08-7.62-10neither
45614561Duane LudwigDarren Elkins3/21/2010Broomfield, Colorado, USAUSABlueLightweightMALE3000Orthodox177.80180.34145152Orthodox177.80177.80170312510-210.002.546neither
45624562John HowardDaniel Roberts3/21/2010Broomfield, Colorado, USAUSARedWelterweightMALE3000Southpaw177.80187.96170093Orthodox170.18180.3417027290-3-307.627.62-2neither
45634563Brendan SchaubChase Gormley3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE3110Orthodox190.50196.00265110Orthodox193.04198.1224527270000-2.54-2.120neither
45644564Mike PierceJulio Paulino3/21/2010Broomfield, Colorado, USAUSARedWelterweightMALE3000Orthodox182.88185.42170161Orthodox172.72177.80170293410-1110.167.62-5neither
45654565Eric SchaferJason Brilz3/21/2010Broomfield, Colorado, USAUSABlueLight HeavyweightMALE3182Orthodox180.34180.34205393Orthodox190.50190.50185323400-12-10.16-10.16-2neither